{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Intro to pyplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.plot([1, 2, 3, 40])\n",
    "plt.ylabel('some numbers')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x83b7278>]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Xjchmyrf70a6FNtOShqFAF4mBPQcP8eCbK3lhfogTO7fihX8ayWm9dJNmaVgKdJEovbdiG3e/vJSSveVMPLMn//rNPrRsprZ9aXgKdJE62rGvnPteW8FrSzbTr2tbpl2fy5CsDvEuS1KYAl2kltydV5ds5r5Xl7OvvJIfnd+HSWf1olmTRvEuTVKcAl2kFjbvOsBPXl7G+6uKOSWrA4+MGUyfE9rGuywRQIEuEpFw2Hl+/kYeemsVVWHnnov7c9OoHG2mJQlFgS5yHOu372fKnELmrS/l9N6defCKwWR3bhXvskT+DwW6yDFUVoV5/JP1/Obdz2nWpBEPXzWIsblZatuXhKVAFzmKlVv2cOecQgqLdnN+/xO4//KBnNCuRbzLEvlaCnSRw5RXVvH799fyhw++oEOrpvz+uqFcOKirZuUSCAp0kRoLvtzJnXMKWVu8jytP7c49F/enozbTkgBRoEvKK6uo5Jdvr+bJv28go10Lnrg5j3P6dol3WSK1pkCXlPbJmu1MmVtI0c4DXD/yRCaP7ktbbaYlAaVAl5S0+8AhHnhjBTMLiuiR1poZE0cyoqc205JgU6BLynl7+VbueXkZO/ZXMOmsXvzwmyfRoqk205LgiyrQzawD8BgwEHBggrt/GovCRGKtZG859726nDeWbuHkjHY8fmMegzLbx7sskZiJdob+n8Cf3X2MmTUD1D4nCcfdeWnRJn72+grKyqu444K+TDyzJ00bazMtSS51DnQzawecCdwE4O4VQEVsyhKJjU27DvDjuUv58PMShmZXb6bVu4s205LkFM0MvSdQAjxhZkOABcAP3H1/TCoTiUI47Dw770sefmsVDtx3SX+uP02baUlyi+ZnzibAUOCP7n4qsB+YcuRBZjbRzArMrKCkpCSK04lE5ouSfVwz7VN++spyhp7Ykbd/eCY3nd5DYS5JL5oZehFQ5O7zar6ezVEC3d2nAdMAcnNzPYrziXytyqow0z5ex2/fW0OLJo345ZjBjBmWqbZ9SRl1DnR332pmITPr6+6rgfOAFbErTSRyyzfv5s45hSzbtIfRA7rys8sH0KWtNtOS1BLtKpfvA8/VrHBZB9wcfUkikTt4qIr/en8NUz9cR8dWzfjjd4by7UEZ8S5LJC6iCnR3XwzkxqgWkVop2FDK5DmFrCvZz1VDM7nn4pPp0EqbaUnqUqeoBM7+8urNtJ76dAPd2rfkqQnDOatPerzLEok7BboEykefl3DX3KVs3n2AG0/L4fYL+tKmuf4ai4ACXQJiV1kF97+xktkLiuiZ3ppZ/+80cnM6xbsskYSiQJeE99bSLdzzynJ2llXwvXN68f1ztZmWyNEo0CVhFe85yE9fWc6fl29lQLd2PDUhjwHdtJmWyLEo0CXhuDuzFxTx89dXcLAyzOTRffmnb2gzLZHjUaBLQgmVlvHjl5by8Zrt5OV05KGrBtMrvU28yxIJBAW6JIRw2Hn60w088vZqDPj5ZQP4zogTaaT9V0QipkCXuFtbvJc75yxlwZc7OatPOg9cMZDMjtpaX6S2FOgSN4eqwkz7aB3/+d4aWjVvzG/GDuGKU7trMy2ROlKgS1ws27SbO2YXsnLLHi4alMF9lw4gvW3zeJclEmgKdGlQBw9V8dv31vDox+vo1LoZU8cPY/TArvEuSyQpKNClwcxfX8qUOYWs276fsbmZ3H1hf9q3ahrvskSShgJd6t2+8koefmsVz3z2JZkdW/LsLSM446S0eJclknQU6FKv/rq6mLvnLmXLnoNMOL0Ht1/Qh1bN9NdOpD7oX5bUi537K/j56yuYu2gTvbu0YfakUQw7sWO8yxJJagp0iSl3542lW7j3leXsPnCIfzm3N987tzfNm2gzLZH6pkCXmNm25yD3vLyMd1ZsY1D39jxzywj6d2sX77JEUkbUgW5mjYECYJO7Xxx9SRI07s7MghD3v7GSisowd327H7ec0YMm2kxLpEHFYob+A2AloKlYCtq4o4y7Xirkb2t3MLxHJx6+ajA90lrHuyyRlBRVoJtZJnAR8ADwo5hUJIFQVlHJ8/M28ut3PqdxI+P+ywdy3fBsbaYlEkfRztB/C0wG2h7rADObCEwEyM7OjvJ0Ek/uzpKi3czID/Haks3sK6/knL7pPHDFILp1aBnv8kRSXp0D3cwuBordfYGZnX2s49x9GjANIDc31+t6PomfXWUVvLRoEzPyQ6zaupcWTRtx0aBuXJOXRV5OR22mJZIgopmhnw5camYXAi2Admb2rLuPj01pEk/hsPPpuh28mB/i7eVbqagMMzizPQ9cMZBLhnSjXQu17IskmjoHurvfBdwFUDNDv11hHnxbdx9k9oIQMwpChEoP0K5FE64bns3Y3CwtQRRJcFqHLhyqCvP+qmJm5If4YHUxYYdRvTpz+7f6csGArrRoqqYgkSCISaC7+wfAB7H4vaThrCvZx4yCEHMWbGL7vnK6tG3ObWf3YmxuFid21tJDkaDRDD3FHKio4q1lW3gxP8T89aU0bmSc268L4/KyOKtPupqBRAJMgZ4ilm3azYv5G3ll0Wb2lldyYudWTB7dlzFDM+nSrkW8yxORGFCgJ7HdZYd4ZUn1csPlm/fQvEkjLhyUwdjcLEb27KTlhiJJRoGeZNydeetLmZEf4s2lWyivDNM/ox0/u2wAlw3prjsEiSQxBXqSKN5zkNkLi5iZH2LDjjLaNm/C1bmZjMvLZmD39vEuT0QagAI9wCqrwnywuoQZBSHeX1VMVdgZ3qMT3z/3JC4clEHLZlpuKJJKFOgB9OWO/cwsCDGroIjiveWktWnGrd/owdjcLHqlt4l3eSISJwr0gDh4qIq3l2/lxfkhPl23g0YGZ/ftwjV5WZzbrwtNtdxQJOUp0BPcis17mFkQ4qVFm9h94BBZnVryb+f3YUxuJhnttcOhiPwvBXoC2nvwEK8u2cyM/BCFRbtp1rgRFwzsyri8LE7r2Vl7jovIUSnQE4S7U/DlTl6cX73c8MChKvqe0JZ7L+nP5ad0p2PrZvEuUUQSnAI9zrbvK2fuwiJezA+xrmQ/rZs15vJTu3FNXjZDMtur+UdEIqZAj4OqsPPRmhJmzA/x3sptVIadYSd25JExvbhoUAatm+uyiEjtKTkaUKi0jFkFIWYtKGLL7oN0at2Mm0/P4Zq8LHp3OeZd/EREIqJAr2fllVW8u2IbM/JDfLJ2OwDfOCmdey7uzzdPPoFmTbTcUERiQ4FeT1Zv3cuM/BAvLSpiZ9khundoyQ/OO4mrc7Porhsqi0g9UKDH0L7ySl5fspkZBSEWbdxF08bGt/p3ZWxeFmf0TqOxlhuKSD2qc6CbWRbwNNAVCAPT3P0/Y1VYULg7i0K7mDE/xGuFmymrqKJ3lzb85KKTueLU7nRu0zzeJYpIiohmhl4J/Ju7LzSztsACM3vX3VfEqLaEVrq/grkLi5iRH2JN8T5aNm3MJUMyuCYvm6HZHbTcUEQaXJ0D3d23AFtqHu81s5VAdyBpAz0cdj5Zu50ZBSHeWb6VQ1XOKVkdePDKQVw8OIO2LbTXuIjET0zeQzezHOBUYF4sfr9Es3nXAWYVFDGzIMSmXQfo0Kop40eeyDV5WfTr2i7e5YmIADEIdDNrA8wBfujue47y+kRgIkB2dna0p2swFZVh/rJyGy/mh/hoTQnucEbvNKZ8ux/n9z+BFk2117iIJJaoAt3MmlId5s+5+9yjHePu04BpALm5uR7N+RrC2uJ9zCwIMWdBETv2V9C1XQv++ZzejM3NIqtTq3iXJyJyTNGscjHgcWClu/8mdiU1vLKKSt4o3MKM/BAFX+6kSSPjvJO7MC4vmzP7pGu5oYgEQjQz9NOB64GlZra45rkfu/ub0ZdV/9ydwqLdvJgf4rUlm9lXXknPtNbc9e1+XDk0k/S2Wm4oIsESzSqXT4DATV13lVXw8qJNvJgfYtXWvbRo2ogLB2UwLi+bvJyOWm4oIoGVEp2i4bDz2bodvJgf4s/Lt1JRGWZQ9/bcf/lALj2lG+203FBEkkBSB/rW3QeZvSDEzIIiNpaW0a5FE67Ny2JsXhYDurWPd3kiIjGVdIF+qCrMX1cVMyM/xF9XFxN2GNmzEz86vw+jB3bVckMRSVpJE+jrt+9nRn6I2QuK2L6vnPS2zZl0Vi/G5maRk9Y63uWJiNS7QAf6gYoq3lpWvdxw3vpSGjcyzunbhWvysjinbzpNGmuvcRFJHYEM9GWbdjMjP8TLizex92AlJ3ZuxR0X9GXMsExOaNci3uWJiMRFYAJ994FDvLq4ernh8s17aNakERcO7Mo1edmM6NGJRmr+EZEUF4hA/91f1vD7v66lvDLMyRnt+PdLB3D5Kd1p30rLDUVEvhKIQO/WoSVjhmUyLi+bgd3bqflHROQoAhHoY4ZlMmZYZrzLEBFJaFoGIiKSJBToIiJJQoEuIpIkFOgiIklCgS4ikiQU6CIiSUKBLiKSJBToIiJJwty94U5mVgJ8Wcf/PQ3YHsNy4kljSTzJMg7QWBJVNGM50d3Tj3dQgwZ6NMyswN1z411HLGgsiSdZxgEaS6JqiLHoLRcRkSShQBcRSRJBCvRp8S4ghjSWxJMs4wCNJVHV+1gC8x66iIh8vSDN0EVE5GskVKCb2XQzKzazZcd43czsd2a21swKzWxoQ9cYqQjGcraZ7TazxTW/ftrQNUbCzLLM7K9mttLMlpvZD45yTCCuS4RjCcp1aWFm881sSc1Y/v0oxzQ3sxk112WemeU0fKXHF+FYbjKzksOuy63xqDUSZtbYzBaZ2etHea1+r4m7J8wv4ExgKLDsGK9fCLwFGDASmBfvmqMYy9nA6/GuM4JxZABDax63BT4H+gfxukQ4lqBcFwPa1DxuCswDRh5xzHeBqTWPxwEz4l13FGO5CfjveNca4Xh+BDx/tL9H9X1NEmqG7u4fAaVfc8hlwNNe7TOgg5llNEx1tRPBWALB3be4+8Kax3uBlUD3Iw4LxHWJcCyBUPNnva/my6Y1v478QOwy4Kmax7OB8ywB798Y4VgCwcwygYuAx45xSL1ek4QK9Ah0B0KHfV1EQP9B1jit5sfMt8xsQLyLOZ6aHw9PpXoGdbjAXZevGQsE5LrU/Gi/GCgG3nX3Y14Xd68EdgOdG7bKyEQwFoCrat7Sm21mWQ1cYqR+C0wGwsd4vV6vSdAC/WjfyQL5nRxYSHU77xDgv4CX41zP1zKzNsAc4IfuvufIl4/yvyTsdTnOWAJzXdy9yt1PATKB4WY28IhDAnNdIhjLa0COuw8G3uN/Z7kJw8wuBordfcHXHXaU52J2TYIW6EXA4d+ZM4HNcaolKu6+56sfM939TaCpmaXFuayjMrOmVAfgc+4+9yiHBOa6HG8sQbouX3H3XcAHwOgjXvqf62JmTYD2JPjbgMcai7vvcPfymi8fBYY1cGmROB241Mw2AC8C55rZs0ccU6/XJGiB/ipwQ82qipHAbnffEu+i6sLMun713pmZDaf6WuyIb1X/V02NjwMr3f03xzgsENclkrEE6Lqkm1mHmsctgW8Cq4447FXgxprHY4D3vebTuEQSyViO+EzmUqo//0go7n6Xu2e6ew7VH3i+7+7jjzisXq9Jk1j9RrFgZi9QvcogzcyKgHup/oAEd58KvEn1ioq1QBlwc3wqPb4IxjIGuM3MKoEDwLhE/MdG9azjemBpzXucAD8GsiFw1yWSsQTlumQAT5lZY6q/6cx099fN7GdAgbu/SvU3r2fMbC3Vs8Bx8Sv3a0Uyln8xs0uBSqrHclPcqq2lhrwm6hQVEUkSQXvLRUREjkGBLiKSJBToIiJJQoEuIpIkFOgiIklCgS4ikiQU6CIiSUKBLiKSJP4/nBDhMTS193MAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([1, 2, 3, 4], [1, 4, 9, 16])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Formatting the style of your plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.plot([1, 2, 3, 4], [1, 4, 9, 16], 'ro')\n",
    "plt.plot([1, 2, 3, 4], [1, 4, 9, 16])\n",
    "plt.axis([0, 6, 0, 20]) # x轴：0-6；y轴：0-20\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0. , 0.2, 0.4, 0.6, 0.8, 1. , 1.2, 1.4, 1.6, 1.8, 2. , 2.2, 2.4,\n",
       "       2.6, 2.8, 3. , 3.2, 3.4, 3.6, 3.8, 4. , 4.2, 4.4, 4.6, 4.8])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# evenly sampled time at 200ms intervals\n",
    "t = np.arange(0., 5., 0.2) # (起,始,步长)\n",
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# red dashes(破折号), blue squares and green triangles\n",
    "plt.plot(t, t, 'r--', t, t**2, 'bs', t, t**3, 'g^') # 指数爆炸\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting with keyword strings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "data = {'a': np.arange(50),\n",
    "        'c': np.random.randint(0, 50, 50), # 随机生成50个0-50区间的整数\n",
    "        'd': np.random.randn(50)}\n",
    "data['b'] = data['a'] + 10 * np.random.randn(50)\n",
    "data['d'] = np.abs(data['d']) * 100\n",
    "\n",
    "# 绘制散点图\n",
    "plt.scatter('a', 'b', c='c', s='d', data=data)  # 原始\n",
    "# plt.scatter(data['a'], data['b']) \n",
    "# plt.scatter(data['a'], data['b'], s=data['d'], c='r') \n",
    "# plt.scatter(data['a'], data['b'], s=data['d'], c='r', data=data) \n",
    "# plt.scatter('a', 'b', s='d', c='r', data=data) \n",
    "\n",
    "plt.xlabel('entry a')\n",
    "plt.ylabel('entry b')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "       17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
       "       34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['a']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3.29017752, 19.48565272, -4.77167221, -0.97375831, -4.64248986,\n",
       "        7.28321391,  5.23591475,  4.48272969, -7.10004629, 10.80041759,\n",
       "       10.0719648 , 11.00050458, 17.43127156,  9.08827267, 16.39785303,\n",
       "       -2.03928697,  3.67779205, 21.15377442, 29.56226843, 27.65409585,\n",
       "       15.82559224, 37.15159083, 14.19040651, 20.55752945, 36.31421294,\n",
       "       30.30721859,  7.82922624, 43.01913864, 15.35166165, 37.56849767,\n",
       "       11.19601998, 26.05664986, 58.68190179, 27.65649688, 24.72612961,\n",
       "       22.10456624, 53.01081886, 27.23154497, 25.51105031, 29.07364447,\n",
       "       45.68507309, 44.28754226, 36.89827737, 44.94351809, 51.22853209,\n",
       "       54.16095266, 29.49473317, 26.32634994, 54.2381007 , 36.35137594])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['b']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2, 27, 24, 37,  9, 23, 27, 38, 10, 37, 31, 32, 26, 10, 44, 37, 24,\n",
       "       16, 38, 43, 28, 29, 20, 33, 23, 21,  2,  8, 30, 36, 40, 25, 14, 32,\n",
       "       43, 23, 22, 43, 27,  2, 24, 14, 22,  4, 36, 27, 40, 25, 19, 43])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['c']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 27.25623882,  21.89965093,  97.44212545,  68.81002326,\n",
       "        66.77471751,  46.72030891,  56.68121157,  99.34545713,\n",
       "       115.81179372,   3.7900131 , 163.27164357, 119.20532256,\n",
       "        62.71112275, 111.78547729, 104.53542436, 204.38007688,\n",
       "        47.29966499,  16.6937068 ,  54.87667133,   8.62720249,\n",
       "         3.39885517,  63.06814777,  30.97445709,   6.27498125,\n",
       "        20.24982985,  23.56338331,  54.94126841,  76.7421823 ,\n",
       "       156.91940782,  73.99086555, 102.2043572 ,  81.79328058,\n",
       "        95.19276732,  28.20632193, 127.96778791, 168.1174148 ,\n",
       "        76.34082432,  22.33781208,  14.82590209,  26.70388126,\n",
       "        58.43602281,  75.06367975, 223.08276343, 102.2913843 ,\n",
       "        62.76492936,   1.2310046 ,  36.80263815, 100.63216715,\n",
       "       123.88452195, 107.56056223])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['d']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Plotting with categorical variables\n",
    "===================================\n",
    "\n",
    "It is also possible to create a plot using categorical variables.\n",
    "Matplotlib allows you to pass categorical variables directly to\n",
    "many plotting functions. For example:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "names = ['group_a', 'group_b', 'group_c']\n",
    "values = [1, 10, 100]\n",
    "\n",
    "plt.figure(figsize=(15, 5)) # figsize(宽度,高度)\n",
    "\n",
    "plt.subplot(1,3,1) #(一行，三列，第一个)\n",
    "plt.bar(names, values) # 柱状图\n",
    "plt.ylabel(\"Money(billion)\")\n",
    "\n",
    "plt.subplot(1,3,2) #(一行，三列，第二个)\n",
    "plt.scatter(names, values) # 散点图\n",
    "\n",
    "plt.subplot(1,3,3) #(一行，三列，第三个)\n",
    "plt.plot(names, values) #\n",
    "\n",
    "plt.suptitle('Categorical Plotting')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Working with multiple figures and axes\n",
    "\n",
    "\n",
    "MATLAB, and :mod:`~matplotlib.pyplot`, have the concept of the current\n",
    "figure and the current axes.  All plotting commands apply to the\n",
    "current axes.  The function :func:`~matplotlib.pyplot.gca` returns the\n",
    "current axes (a :class:`matplotlib.axes.Axes` instance), and\n",
    ":func:`~matplotlib.pyplot.gcf` returns the current figure\n",
    "(:class:`matplotlib.figure.Figure` instance). Normally, you don't have\n",
    "to worry about this, because it is all taken care of behind the\n",
    "scenes.  Below is a script to create two subplots.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x444.96 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def f(t):\n",
    "    return np.exp(-t) * np.cos(2*np.pi*t)\n",
    "\n",
    "t1 = np.arange(0.0, 5.0, 0.1)\n",
    "t2 = np.arange(0.0, 5.0, 0.02)\n",
    "\n",
    "plt.figure(figsize=(10,6.18)) # 黄金分割尺寸\n",
    "plt.subplot(211) # 2行1列的第1个\n",
    "\n",
    "# plt.plot(t1, f(t1), 'bo', t2, f(t2), 'k') # 等于plt.plot(t1, f(t1), 'b-o')加plt.plot(t2, f(t2), 'k') # k(黑色)\n",
    "plt.plot(t1, f(t1), 'b-o')\n",
    "plt.plot(t2, f(t2), 'k') # k(黑色)\n",
    "\n",
    "plt.subplot(212) # 2行1列的第2个\n",
    "plt.plot(t2, np.cos(2*np.pi*t2), 'r--')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Working with text\n",
    "\n",
    "The :func:`~matplotlib.pyplot.text` command can be used to add text in\n",
    "an arbitrary location, and the :func:`~matplotlib.pyplot.xlabel`,\n",
    ":func:`~matplotlib.pyplot.ylabel` and :func:`~matplotlib.pyplot.title`\n",
    "are used to add text in the indicated locations (see :doc:`/tutorials/text/text_intro`\n",
    "for a more detailed example)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "mu, sigma = 100, 15\n",
    "x = mu + sigma * np.random.randn(10000)\n",
    "\n",
    "# the histogram(n.柱状图) of the data. patches(n.补丁)\n",
    "# 50个柱子\n",
    "n, bins, patches = plt.hist(x, 50, density=1, facecolor='k', alpha=0.5)  # plt.hist()绘制柱状图\n",
    "# n, bins, patches = plt.hist(x, 5, density=1, facecolor='k', alpha=0.75)  # plt.hist()绘制柱状图\n",
    "\n",
    "plt.xlabel('Smarts')\n",
    "plt.ylabel('Probability')\n",
    "plt.title('Histogram of IQ')\n",
    "\n",
    "# 坐标轴(x,y)的取值范围\n",
    "plt.axis([40, 160, 0, 0.03])\n",
    "\n",
    "# 在图表中添加文本，(x轴,y轴,文本内容) # to add text in an specified location\n",
    "# plt.text(120, 0.025, r'$\\mu=100,\\ \\sigma=15$') \n",
    "plt.text(120, 0.025, r'$\\mu=100,\\sigma=15$') \n",
    "\n",
    "plt.grid(True) # 是否展示网格 \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Annotating text\n",
    "\n",
    "The uses of the basic :func:`~matplotlib.pyplot.text` command above\n",
    "place text at an arbitrary position on the Axes.  A common use for\n",
    "text is to annotate(v.注释，注解) some feature of the plot, and the\n",
    ":func:`~matplotlib.pyplot.annotate` method provides helper\n",
    "functionality to make annotations easy.  In an annotation, there are\n",
    "two points to consider: the location being annotated represented by\n",
    "the argument ``xy`` and the location of the text ``xytext``.  Both of\n",
    "these arguments are ``(x,y)`` tuples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "ax = plt.subplot(111) #(一行，一列，第一个)\n",
    "\n",
    "t = np.arange(0.0, 5.0, 0.01)\n",
    "s = np.cos(2*np.pi*t)\n",
    "\n",
    "# 1w=2（线条的粗度）\n",
    "line, = plt.plot(t, s, lw=2)\n",
    "\n",
    "plt.annotate('local max', xy=(2, 1), xytext=(4, 1.5),\n",
    "             arrowprops=dict(facecolor='black', shrink=0.05),\n",
    "            )\n",
    "\n",
    "# plt.ylim(-2, 2)\n",
    "plt.ylim(-2, 2) # y轴的区间范围\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logarithmic and other nonlinear axes\n",
    "Logarithmic(adj.对数的)、nonlinear(adj.非线性的)\n",
    "\n",
    ":mod:`matplotlib.pyplot` supports not only linear axis scales, but also\n",
    "logarithmic and logit scales. This is commonly used if data spans many orders\n",
    "of magnitude. Changing the scale of an axis is easy:\n",
    "\n",
    "    plt.xscale('log')\n",
    "\n",
    "An example of four plots with the same data and different scales for the y axis\n",
    "is shown below.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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l/LljSBuk+7hEBoJA0FlXXs/qHXW8unk3y7bWsKO2aw3yU6aP4IKjCjjzsHymjMyKcqQivYtY0mlmZwF3AMnAve7+/R77xwO/BnJDZb7m7ksiFZ9IIivd08z1D7/JitJaLpg7lh9fcmS0Q5IBSlMmRdbbFQ08vrKclWW1rCit3TcXZt6QNBZMGsaF8wo4fvJwFkwaHuVIRT5YRJJOM0sG7gJOB8qAZWa22N3Xdit2E/AHd/+Fmc0ClgCFkYhPJFFVN7Zxy+I1PLl6F4Ggc+PZM/j08ROjHZYMYJoyKfzK61r4x/pKnl5TwYtvVwFdXeaLZuZz/JQ8ZhfkMCkvU93lEnMidaVzPlDi7psBzOwR4Hyge9LpQHbocQ6wM0KxiSQUd+eJVeXc/9IWNlY00tYZ5OKiAi45ejxHjtNAIZFI293YxoaKBjZVNrJ4xU6Wba0BYNywDL5w2lQ+Nq+AccMGRzlKkUMXqaRzLFDa7XkZsKBHmVuAZ8zs34FMYFFkQhNJDFUNbfztrR0sWVXOG9trGTo4hXOPHMNlR49ndkFOtMMTSTi/eWUrS1aVs3TLnn3TGOUOTuHLi6ZxzhGjmDxiiK5mSlyJVNLZ26fGezy/DHjA3X9kZscCD5rZ4e4efNeBzK4FrgXIz8+nuLi4z0E0NjYeUPlYlkh1hcSq74HWNejOX0s6eGJzBwGHMZnGFTNTOXHsINIH7WZ3yW6KS8IX76FIpPMqiWFLdRP/WF/Jn18vY215PZPyMrnmxEmcOHUEU0YOIT87TYmmxK1IJZ1lwLhuzwt4b/f5VcBZAO7+ipmlA3lAZfdC7n4PcA9AUVGRL1y4sM9BFBcXcyDlY1ki1RUSq74HUtcX367iS4+8SW1zBydPG8F/f3gWU0YOCW+A/SiRzqvEr111rfxzYxV/e2snL5VUA3DYmGz+5/zDuHzBBE1HJgkjUknnMmCqmU0EdgCXApf3KLMdOA14wMxmAulAVYTiE4k7xRsq+fLv3yI3I4VvnXcYHz5ijP64iURIIOg8t66Ce17czFultXQGnTE56dxw5nTOPWIM44frHk1JPBFJOt2908yuB56mazqk+919jZl9G1ju7ouB/wD+z8y+TFfX+6fcvWcXvIh8gNU76vjzG2X89tXtjM5N56eXHaV7NkXCyN33dZtvrm5iRUkLX3v5H+yqbyU/O43LF4znY/PGcdiYbJL0xU8SWMTm6QzNubmkx7abuz1eCxwfqXhE4k1bZ4AvPvwWT63ZRUqysWDicH588RxGZqdHOzSJI5qns0tnIMjTayp4bn0FS7fsoayma5L2nIwUhqfC3PG5nHPEaBbNzCc9RQssiIBWJBKJC7vqWrnmN8tZtaOOa06cyHUnTyZvSFq0w5I4lOjzdDa1dfKjZ97mwVe30hFwstIHccyk4Vx1wkSOn5LHtPys0L3I86IdqsiAo6RTJMZtqmrkyntfY1d9K9edNIn/OmuGuvBE+lFnIMj6XQ3c+8/N/PWtrjGwi2aO5IxZozjvyDG6kinSR0o6RWJURyDIHX/fyL0vbSYjJZk/fe44jho/NNphicS0zkCQDRUNbN/dzLpdDawrr2f51j37lp88b84YzpszhkWz8qMcqUjsUdIpEmM6g86Dr27jV//awuaqJk6flc93PnI4+bp3U+SQlNU0c8MfV/LK5t0AJBlMzMvkxKkjOGXGCIomDNPKQCKHQEmnSAxpbOvkp2+2saJqNVNGDuHnVxzFh2aPjnZYIjHL3Vm6ZQ93FW/ixberGJRk/PupUzhj1iim5g9R17lIP1LSKTKArSitZUVZLdt2N7N6Rx2vb6uhM+jcdM5Mrj5xUrTDE4lpgaDzhUfe5ImV5eQNSeWzJ0/m4qICJo2InQUURGKJkk6RAai8roVbl6xn8YquQQvpKUlMy8/iqhMnMqxlpxJOkUNQ1dDGg69u47EVO9lS3cSZh+Vz24VHkDs4NdqhicQ1JZ0iA0gw6Px26XZ+8uzb1LV0cO1Jk/jUcYWMzknftx5zcXFFlKMUiV1Pr9nFFx5+k/ZAkCMKcvneR2dz+YLx0Q5LJCEo6RQZILbtbuLWJet5as0uDh+bzU8vn8txk/OiHZZIXHi7ooH/eXwt/9xYzZyCHG6/5Eh1o4tEmJJOkSirbmzjf5/awKNv7cCA606axFfPmqF10kX6yYOvbuOWxWvIzUjh6hMm8rmFkxmuxRNEIk5Jp0iUVNa38qc3yvjlC5tpbu/kkqPH8W8LpzAmNyPaoYnsV6wtg1m6p5l7XtxEksHvrzuWKSN1dVMkWpR0ikRYMOj85LmN3PV8CYGgc8KUPG45bxZTRmZFOzSRDxRLy2D++fUybnx0FUkGd398nhJOkShT0ikSIS3tAX5eXMKzaytYv6uB02fl859nTGf6KCWbIv2ptrmd255az8NLS5kxKot7rixi/HBN6i4SbUo6RcLM3bnvpS3c9XwJtS0dzC8cxnc/ejiXzx+/b0S6iBy6nbUt/PWtHfz21e3sqG1h7vhc7v1Eke7fFBkglHSKhNGO2ha++qcV/KtkN0eOy+X2RVNZOH1ktMMSiTs7als46/YXaWjrZO74XH508RyOmTQ82mGJSDdKOkXCIBh0vrtkHfe9tAWAm86ZyaePn6gR6SJh0NYZ4Cu/f4vWzgBPfvFEZo7OjnZIItILJZ0i/SwYdL78h7f421s7WTRzJNefOpUjx+VGOyyRuBQIOrc9uYHXtuzhP8+YpoRTZABT0inST1aU1vL4yp38Y30lm6qauObEiXz9QzN136ZImNQ1d3DZ/73K2vJ6zpk9mutPnRrtkETkfSjpFOkH5XUtfPy+12ho7eSo8bncfskczp8zVgmnSJh0BoJ84ldLWVtez/cvmM0lR4+Ldkgi8gGUdIocoobWDj774OsEgs7z/7mQiXmZ0Q5JJK61dgS45JevsKKsjs8cP5FL52vtdJFYELGk08zOAu4AkoF73f37vZS5GLgFcGCFu18eqfhEDlRdcwfff2o9j63YSWNbJz+48AglnCJhVlLZwNW/Xs7W3c388GNzuPCosdEOSUT6KCJJp5klA3cBpwNlwDIzW+zua7uVmQrcCBzv7jVmpnllZMDaWdvC1b9eztryeo6dNJzPLpzMydNGRDsskbh330tb2VXfys+vOIoPzR4d7XBE5ABE6krnfKDE3TcDmNkjwPnA2m5lrgHucvcaAHevjFBsIn3W1hng589v4ufFJbjDDy46gouLdC+ZSCSUVDbwx+WlXHhUgRJOkRgUqaRzLFDa7XkZsKBHmWkAZvYvurrgb3H3pyITnsgHW751D5996HWqG9s5dcZIbjhzuqZnEYmQdeX13PCnFQxOTeaGs6ZHOxwROQiRSjp7G8LrPZ4PAqYCC4EC4J9mdri7177rQGbXAtcC5OfnU1xc3OcgGhsbD6h8LEukukJ469sZdJZs6eAvGzvITjU+NyeNBaObqNjwBhUbwvKW7yuRzm0i1VX2z9353EOvs3V3Mz+/4ijytKylSEyKVNJZBnTvgywAdvZS5lV37wC2mNkGupLQZd0Lufs9wD0ARUVFvnDhwj4HUVxczIGUj2WJVFcIX303VzXy5T+sYEVpMydNG8FVJ0yM+r2biXRuE6mu0WRmk4BvADnuflG04+np2bUVbN3dzI1nz1C3ukgMS4rQ+ywDpprZRDNLBS4FFvco81fgFAAzy6Oru31zhOITeZfdjW38+Nm3ufiXr7KitJbvfvRwfvOZ+VFPOEV6MrP7zazSzFb32H6WmW0wsxIz+9r7HcPdN7v7VeGN9OAEg86Pn32bJINPHz8x2uGIyCGIyJVOd+80s+uBp+m6X/N+d19jZt8Glrv74tC+M8xsLRAAbnD33ZGIT2Svbbub+K8/r+S1LXtwh3kThnLnZUdy3OS8aIcmsj8PAD8DfrN3w/5mDKGr/b21x+s/M5AHbj67roL1uxr4yJFjSB0UqeskIhIOEZun092XAEt6bLu522MHvhL6EYmYQNB5fVsNP/3HRv65sZrM1GS+dNo0Tps5ksPH5kQ7PJH35e4vmllhj829zhji7rcCH45shAevsr6V//rzSsbmZvDDj82Jdjgicoi0IpEkrGDQ+c4T6/jrWzvY09TO4NRkvrxoGh8rKmBMbka0wxM5FH2ZMWQfMxsOfBeYa2Y3hpLT3sod1EDOgx0Q9ocN7dQ2d/C5w5N56Z8vHvDroyWRBsCprvErHPVV0ikJZ2t1E0+sKufpNbtYWVbHwukjuPCoAk6ePoLs9JRohyfSH/oyY8g7O7puZfrsBx30YAdyHsyAsEffLGPJlhWcM3s0111w1AG9NtoSaQCc6hq/wlFfJZ2SMGqa2rn7hU3c+9IWAkFnen4W3z7/MK48ZgJmvf2NFolZfZkxZMAKBJ1fFG9ibG4GXz9nZrTDEZF+oqRT4l5tczu/W7qdnz5XQktHgBmjsvjllfOYMFzrpEvc2jdjCLCDrhlDLu+PA5vZucC5U6ZM6Y/D9eqXL27i7YpG/veiIxirW11E4oaSTolb7s4fl5dx65PrqGnu4JhJw7jhzOnMmzAs2qGJ9Bsze5iuRTXyzKwM+Ka739fbjCH98X7u/hjwWFFR0TX9cbyeXt5UzY+feZsPzR7FRfMKwvEWIhIlSjolLlXWt/LZh17nje21HDYmmzsuncuJU/PUjS5xx90v28/298wYEgse+NdWhmamcutHj9DnVSTOKOmUuPJ2RQM3/mUVK0prSR2UxM0fnsWnjiskKUl/vET6Q7i71+taOpiUl0nOYA3qE4k3SjolLrQHnIeXbufO5zbS3hnkymMn8NG5YzmiIDfaoYnElXB3rze1dzIyKz0chxaRKFPSKTGrurGNx1fs5F+bdvPyxmaaOlYxaUQmv7xynpJNkRjV3BYgM09/mkTikT7ZEpM2VjTw6QeWUVbTQuHwwRwxIpl//1ARx04ervvARGJYU3snmanJ0Q5DRMJASafElJLKBv64vIwHX91G2qAkfnf1Ao6dPJwXXniB46ZofXSRcAv3PZ1NbQEGp+pPk0g80idbBry2zgAvbazmqdW7+MubO3B3Fk4fyedPmazpj0QiLJz3dLa0B2hq7yQrXX+aROKRPtkyoK3fVc9//GEFa3bWk56SxJXHTODfFk5mZLYGGojEm5c3VeMORYVDox2KiISBkk4ZkNo6A/xhWSm3Prme5vYAd1x6JGceNor0FN3rJRKv/rmxmoyUZOZPVA+GSDxS0ikDRkt7gD++XsrjK8t5Y1sNnUGncPhgvnfBbI6brPs1RQaCcN7TWVLZyLT8IaQN0pdLkXikpFOirnRPM3c9X8ITK8tpaOtkbG4GHz9mAsdPyeP0WfnRDk9EugnXPZ3tnUHWlddz0rQR/XlYERlAlHRKVFQ3tvH8+kr++tYOXt60G3dYNHMklx49nlNmjCRZKwiJJJS7X9jE7qZ2zjtyTLRDEZEwUdIpEVW6p5k/Li/l/n9tpbGtk5FZafzbwslcXDSOCcMzox2eiETBsq17+PGzb7Ng4jBOnqornSLxSkmnRERzeyd3PLeRX720lY5gkJOnjeA/z5jOzNHZuqopksBe27yba36znKy0Qfzs8qNIUnsgEreUdEpYrSyr5aFXt7Fk1S4a2zo554jRfPG0qUzLz4p2aCJyEPpzIFFrR4CvP7qKrPQUHrxqPiOy0g49QBEZsJR0Sr9r7Qhw+7Nv8+TqXWzf08zg1GTOmT2aS44eR1GhpkIRiWX9OZDoqdW72FTVxE8vm8ukEUP6IToRGcgilnSa2VnAHUAycK+7f38/5S4C/ggc7e7LIxWf9I9tu5u47sHXWb+rgRmjsrjl3FlcMK+A7PSUaIcmIgPItt1NfOPRVRQMzeCc2aOjHY6IREBEkk4zSwbuAk4HyoBlZrbY3df2KJcFfAF4LRJxSf9paO3gd69t587nNtIZdL7zkcP5+DEToh2WiAxQD7y8lY6A84sr5uk+TpEEEakrnfOBEnffDGBmjwDnA2t7lPsf4AfAf0YoLukH7s5nHljGsq01zBqdzV1XHMXEPI1EF5H9e7lkNwsmDWN2QU60QxGRCIlU0jkWKO32vAxY0L2Amc0Fxrn742ampDMGtHYEeOjVbTz46ja27W5baukpAAAgAElEQVTmpnNmcvWJk6IdlogMcKt31LGhooELjhob7VBEJIIilXT21nfi+3aaJQG3A5/6wAOZXQtcC5Cfn09xcXGfg2hsbDyg8rEsnHVtbHde2dnJE1s6qG1zpg9N4pLpqRR2bKO4eHtY3vMDY9K5jUuJVNdY0R+j1x99cwdmcMFRBf0XmIgMeJFKOsuAcd2eFwA7uz3PAg4His0MYBSw2MzO6zmYyN3vAe4BKCoq8oULF/Y5iOLiYg6kfCwLR11b2gM88PJW7vpXCY1tncwvHMZXzpjGMZOG9+v7HAyd2/iUSHWNFf0xen1XfSsTh2dqiiSRBBOppHMZMNXMJgI7gEuBy/fudPc6IG/vczMrBv5To9cHho5AkK//ZRVPrCqnuT3ASdNG8IVTpzBvwlBCXxJERPrE3VlRWkvB0IxohyIiERaRpNPdO83seuBpuqZMut/d15jZt4Hl7r44EnHIgdvd2MYnf7WU1TvqOeeI0Vx5zAQWTBymZFNEDsrSLXsoq2nhsvnjox2KiERYxObpdPclwJIe227eT9mFkYhJ9s/d+XnxJn72jxICQefb5x/GlcdMULIpIofkydW7SBuUxKeOK4x2KCISYVqRSN7j7YoGbvjTSlaU1jK/cBg3nDWdo7WSkIj0g1U76jhyXC6ZafrzI5Jo9KkXAJraOvnrWztYs7Oe59ZV0NQW4OYPz+KTxxWSrImbRaSf7Kxt4bjJeR9cUETijpLOBNcRCPLYip3c/ve3Kd3TQk5GCrNGZ/OVM6bp6qaI9KtA0Kmob2VMbnq0QxGRKFDSmcCCQeebi9fwu9e2Mykvk3s/UcRpM0fqvk0R2a9DmaezobWDoMPQwan9H5iIDHhJ0Q5AoqOhtYOrfr2M3722nXPnjOG5/ziZRbPylXCKyPty98fc/dqcnANfvrK+pROA7IyU/g5LRGKArnQmmJb2AHc8t5E/v1FGVUMb/7ZwMl9cNFXJpoiEXV1LBwA5SjpFEpKSzgTh7qwoq+Omv65i9Y56TpsxkutOnsz8ibpvU0QiQ0mnSGJT0pkA3thew93Fm3hmbQVD0gZx98eP4qzDR0c7LBFJMHuTzuwM/ekRSUT65Mex5vZO7nq+hLue34QZXL5gPDecMZ2hmbqJX0Qir75VVzpFEpmSzjhTuqeZVzbv5tEVrXxr+UtsqW7ixKl5/PBjc8jP1jQlIhI9+650pivpFElESjrjQDDoLN9Ww/0vbeGpNbsAyE0zZo5N56tnTufs2epKF5Hoq2vpYFCSMTg1OdqhiEgUKOmMYe7OH18v446/b2RHbQuZqclceFQBly8YT93mtzj1lGOiHaKIyD7VDW3kDk7RbBkiCUpJZ4xas7OOzz70OqV7Whg6OIUffmwOZx6WT1ao26p4ixp1ERlYlm3dw5HjhkY7DBGJEiWdMaalPcADL2/ltqfWk5U+iBvPnsEnjyskPUXdVSIysO2qb+WMw0ZFOwwRiRIlnTGiqa2Tf6yv5IfPbGDb7mYKhmZw7yeLmDEqO9qhiYh8oNaOAK0dQXIHaxCRSKJS0jlANbR2sLmqibXl9bxUUs2LG6poaOskO30Qt104m0uOHh/tEEUkAR3s2us1ze2A1l0XSWRKOgeg5Vv38PH7XqO1IwjA8MxUTpo+gkuKxjF/4jB1pYtI1Lj7Y8BjRUVF1xzI62qbu6ZLytUcnSIJS0nnALK7sY27nt/Eb1/bRnpKMj+4aA5zCnIoGDqY5CQNDBKR2NXU1gnAkHT92RFJVPr0DwDuzu+WbueHT2+gprmDk6aN4NYLZjM2NyPaoYmI9Ium9gAAg1P1Z0ckUenTPwDc8+Jmbn1yPWNzM3jk2mOZPior2iGJiPSr5tCVTk0ML5K4IpZ0mtlZwB1AMnCvu3+/x/6vAFcDnUAV8Bl33xap+CLN3fnbWzv5/bJSXtm8m6MLh/KbzywgQw2yiMSh5tCVzkxd6RRJWBH59JtZMnAXcDpQBiwzs8XuvrZbsTeBIndvNrPPAT8ALolEfJHW1hngynuXsnTrHkZmpfFvCydz/alTlHCKSNxqbg9d6UxTOyeSqCL1lXM+UOLumwHM7BHgfGBf0unuz3cr/yrw8QjFFlFPrd7Fz4tLWFlWx41nz+DqEydpkJCIxL137ulU0imSqCKVdI4FSrs9LwMWvE/5q4AnwxpRhLV2BPjGo6v58xtljM3N4OYPz+IzJ0yMdlgiIhHR3B7ADNIHKekUSVSRSjp7u5TnvRY0+zhQBJy8n/3XAtcC5OfnU1xc3OcgGhsbD6h8f1pa3smfV7QxfWgSNxxtDOrcRnFx+G5ZjWZdoyGR6qu6SixqbutkcEoySerZEUlYkUo6y4Bx3Z4XADt7FjKzRcA3gJPdva23A7n7PcA9AEVFRb5w4cI+B1FcXMyBlO8PnYEgvyjexH1rShiZlcZj/3EqqYOSwv6+0ahrNCVSfVVXiUXNHQEyNIhIJKFFqgVYBkw1s4nADuBS4PLuBcxsLvBL4Cx3r4xQXGG1q66Vf3/4DZZtreHkaSO47cIjIpJwiogMNM1tnWRqEJFIQotI0ununWZ2PfA0XVMm3e/ua8zs28Byd18M/C8wBPijmQFsd/fzIhFff9ta3cSS1eXc+dxGWjuCfHTuWH70sTnqVhKRhNXUHiBDS/iKJLSI9XW4+xJgSY9tN3d7vChSsYTT/z69nrue3wTAsZOG819nz2BOQQ6hRFpEJCG1tAfITFP3ukgiUwvQjx59s4y7nt/EObNH87WzZzBu2OBohyQiMiA0tXcyREmnSEJTC9APWjsCPLx0O995Yh0zR2fzPx85nGGZqdEOS0SkT8zsI8A5wEjgLnd/pr/fo7ktwMistP4+rIjEEI1qOUQ1Te2c+9OX+NZja5k7Lpdff+ZoJZwiEjFmdr+ZVZrZ6h7bzzKzDWZWYmZfe79juPtf3f0a4FOEaSW45o5OLYEpkuDUAhyC4g2V3PCnlVQ1tPGDi47g4qJxH/wiEZH+9QDwM+A3ezfsb+lhugZy3trj9Z/pNmPITaHX9bvmtoCWwBRJcEo6D0Ig6Fz162UUb6hiWv4QfvixOZw8bUS0wxKRBOTuL5pZYY/NvS497O63Ah/ueQzrGun4feBJd38jHHE2tXcyWFc6RRKaWoCDcPcLmyjeUMVF8wr473NmkTM4JdohiYh0d6BLD/87sAjIMbMp7n53b4UOdkW4+oZGWjuMyp2lFBdX9Ok1sSyRVtJSXeNXOOqrpPMAVDe28a3H1vLYip2cN2cMP7jwCM29KSIDUZ+XHgZw9zuBOz/ooAe7ItzjzzwPNHPYtCksPGlSn14TyxJpJS3VNX6Fo75KOvvI3fn8b9/gtS17uPKYCfz3h2cp4RSRgapPSw9HyqrqAACFeZnRCkFEBgCNXu8Dd+frj67itS17uO7kSfzPRw7XcpYiMpDtW3rYzFLpWnp4cX8c2MzONbN76urq+vyauraui6wLJg3rjxBEJEYpc/oALe0BPv3AMh5eWso5R4zmC6dOjXZIIiL7mNnDwCvAdDMrM7Or3L0T2Lv08DrgD+6+pj/ez90fc/drc3Jy+vya5k7HDIZoIJFIQlML8D7ermjgintfo6qhja+cPo3rT5miLnURGVDc/bL9bH/P0sPR0tzhZKenqP0USXBKOnvh7tz30hZuf/Ztksx4+JpjOHby8GiHJSISdWZ2LnDulClT+vya5k7IztCfG5FEp+71XryyeTffeWIds8Zks+SLJyrhFBEJOZju9d0tQfKGaAlMkUSnpLMXm6uaALjzsrmMGzY4ytGIiMS27Q1BDhuTHe0wRCTKlHT2YkVpLSnJRn5WerRDERGJae5OSycMy9SVTpFEp6Szh39urOLPb5TxkSPH6qZ3EZEeDnTKpPZAEIA0TTMnkvB0ZzdQVtPM717bznPrKtlQ0cDY3AxuOmdWtMMSERlw3P0x4LGioqJr+lK+vVNJp4h0Seiks7KhlR89/TZ/ebOMQNCZXZDLjWfP4NL548nJ0HrqIiKHqi2UdGpBDRFJ2KTzqdXlfPGRtwgEnYvmFfD5U6Zo0JCISD/be6UzNVlJp0iiS8ik89m1FXz2oTcYkZXGPVfOY+74odEOSUQkJhzoPJ17r3SmpSjpFEl0CdcKNLV18t0n1jJpRCb//OopSjhFRA7Agc7T+c6VzuRwhiUiMSBiSaeZnWVmG8ysxMy+1sv+NDP7fWj/a2ZW2N8xBN35t9++wdbdzXztrBmkp6gRFBEJp7bOAKCBRCISoaTTzJKBu4CzgVnAZWbWc3j4VUCNu08Bbgdu688Y2juDPFrSwQtvV/H1D83gjMNG9efhRUSkF+0aSCQiIZFqBeYDJe6+2d3bgUeA83uUOR/4dejxn4DTzKxfJsps7wzyyfuX8timDuaOz+VTx03sj8OKiMgHaNOUSSISEqlWYCxQ2u15WWhbr2XcvROoA/pl0fOUZKOocChXzkrlL587Tt+4RUQO0oFODp83JI0Txg4iP1srvIkkukiNXu/tiqUfRBnM7FrgWoD8/HyKi4v7FMC8VGgc1sYLL7zQp/KxrrGxsc//N/Egkeqruko0Hejk8NNHZXH17DQK8zLDHJmIDHSRSjrLgHHdnhcAO/dTpszMBgE5wJ6eB3L3e4B7AIqKinzhwoV9DqK4uJgDKR/LEqmukFj1VV1FRCQWRaqfeRkw1cwmmlkqcCmwuEeZxcAnQ48vAv7h7u+50ikiIiIisSciVzrdvdPMrgeeBpKB+919jZl9G1ju7ouB+4AHzayEriucl0YiNhEREREJv4itSOTuS4AlPbbd3O1xK/CxSMUjIiIiIpGjYdwiItJnBzp6XURkL4vl2ybNrArYdgAvyQOqwxTOQJNIdYXEqq/q2rsJ7j4inMHIOw6w/U2k31lIrPqqrvGr39vfmE46D5SZLXf3omjHEQmJVFdIrPqqrhJrEu08JlJ9Vdf4FY76qntdRERERMJOSaeIiIiIhF2iJZ33RDuACEqkukJi1Vd1lViTaOcxkeqrusavfq9vQt3TKSIiIiLRkWhXOkVEREQkChIi6TSzs8xsg5mVmNnXoh1PfzCzcWb2vJmtM7M1ZvbF0PZhZvasmW0M/Ts0tN3M7M7Q/8FKMzsqujU4cGaWbGZvmtnjoecTzey1UF1/H1piFTNLCz0vCe0vjGbcB8rMcs3sT2a2PnR+j43z8/rl0O/wajN72MzS4/XcJqJ4a3/V9sZv2wuJ1f5Go+2N+6TTzJKBu4CzgVnAZWY2K7pR9YtO4D/cfSZwDPD5UL2+Bjzn7lOB50LPoav+U0M/1wK/iHzIh+yLwLpuz28Dbg/VtQa4KrT9KqDG3acAt4fKxZI7gKfcfQYwh646x+V5NbOxwBeAInc/nK5lci8lfs9tQonT9ldtb3x/PhOi/Y1a2+vucf0DHAs83e35jcCN0Y4rDPX8G3A6sAEYHdo2GtgQevxL4LJu5feVi4UfoICuD/upwOOA0TVp7aCe5xl4Gjg29HhQqJxFuw59rGc2sKVnvHF8XscCpcCw0Ll6HDgzHs9tIv4kQvurtjd+Pp+J1P5Gq+2N+yudvPMfu1dZaFvcCF3mngu8BuS7ezlA6N+RoWKx/v/wE+CrQDD0fDhQ6+6doefd67OvrqH9daHysWASUAX8KtSdda+ZZRKn59XddwA/BLYD5XSdq9eJz3ObiGL69/ODqO0F4uvzmTDtb7Ta3kRIOq2XbXEzZN/MhgB/Br7k7vXvV7SXbTHx/2BmHwYq3f317pt7Kep92DfQDQKOAn7h7nOBJt7pyulNLNeV0L1R5wMTgTFAJl1dVj3Fw7lNRHF7vtT2vku8fD4Tpv2NVtubCElnGTCu2/MCYGeUYulXZpZCV6P3W3f/S2hzhZmNDu0fDVSGtsfy/8PxwHlmthV4hK5unp8AuWY2KFSme3321TW0PwfYE8mAD0EZUObur4We/4muRjAezyvAImCLu1e5ewfwF+A44vPcJqJY//3sldreuP18JlL7G5W2NxGSzmXA1NCIrFS6bpRdHOWYDpmZGXAfsM7df9xt12Lgk6HHn6TrfqO92z8RGm13DFC3t7tgoHP3G929wN0L6Tp//3D3K4DngYtCxXrWde//wUWh8jHx7dPddwGlZjY9tOk0YC1xeF5DtgPHmNng0O/03vrG3blNUHHX/qrtjc+2FxKu/Y1O2xvtm1kj8QN8CHgb2AR8I9rx9FOdTqDr0vZK4K3Qz4fousfiOWBj6N9hofJG1yjSTcAqukasRb0eB1HvhcDjoceTgKVACfBHIC20PT30vCS0f1K04z7AOh4JLA+d278CQ+P5vALfAtYDq4EHgbR4PbeJ+BNv7a/a3vhte0N1SJj2Nxptr1YkEhEREZGwS4TudRERERGJMiWdIiIiIhJ2SjpFREREJOyUdIqIiIhI2CnpFBEREZGwU9IpIiIiImGnpFNEREREwk5Jp4iIiIiEnZJOEREREQk7JZ0Sd8zsATP7TrTjEBGJNWa21cwWHeIx7jaz/+6vmCR+DIp2ACIiIhI/3P2zex+b2ULgIXcviF5EMlDoSqeIiIiIhJ2STok4M/svM9thZg1mtsHMrjCzZjMb3q3MPDOrMrMUM/uUmf3LzG43s1oz22xmx4W2l5pZpZl98n3e7xozKzGzPWa22MzGdNt3RiiGOjP7uZm9YGZXh/v/QERkIDOzNDP7iZntDP38xMzSuu3/qpmVh/ZdbWZuZlNC+x4ws++YWSbwJDDGzBpDP2P2954S/5R0SkSZ2XTgeuBod88CzgReBYqBi7sV/TjwiLt3hJ4vAFYCw4HfAY8ARwNTQmV/ZmZDenm/U4FbQ8ceDWwLvRYzywP+BNwYOu4G4Lj+q62ISMz6BnAMcCQwB5gP3ARgZmcBXwEW0dUGn9zbAdy9CTgb2OnuQ0I/OyMQuwxQSjol0gJAGjDLzFLcfau7bwJ+TVfyiJklA5cBD3Z73RZ3/5W7B4DfA+OAb7t7m7s/A7TT1fj1dAVwv7u/4e5tdCWYx5pZIfAhYI27/8XdO4E7gV39X2URkZhzBV1tbKW7VwHfAq4M7bsY+JW7r3H35tA+kQ+kpFMiyt1LgC8BtwCVZvZIqLvlb3QlopOA04E6d1/a7aUV3R63hI7Vc9t7rnQCY+i6urn3/RuB3cDY0L7SbvscKDvoyomIxI93tZ2hx2O67Svttq/7Y5H9UtIpEefuv3P3E4AJgAO3uXsr8Ae6vl1fybuvch6KnaH3ASB0j9FwYAdQDhR022fdn4uIJLB3tZ3A+NA26NF20tXztD/ez3FJDFPSKRFlZtPN7NTQDemtdF2hDIR2/wb4FHAe8FA/veXvgE+b2ZGh9/we8Jq7bwWeAGab2UfMbBDweWBUP72viEgsexi4ycxGhO5/v5l32uU/0NWuzjSzwaF9+1MBDDeznPCGK7FASadEWhrwfaCarvsnRwJfB3D3fwFB4I1QUnjI3P054L+BP9P17XwycGloXzXwMeAHdHW5zwKWA2398d4iIjHsO3S1hyuBVcAboW24+5N03QP/PFACvBJ6zXvaTndfT1cCuzk0+4hGrycw67qNTWRgMLN/AL9z93uj8N5JdN3TeYW7Px/p9xcRiUVmNhNYDaSFBmWK9EpXOmXAMLOjgaPoGp0eqfc808xyQ13vXweMrimcRERkP8zso2aWamZDgduAx5RwygdR0ikDgpn9Gvg78CV3b4jgWx8LbKKru/9c4CPu3hLB9xcRiUXXAVV0tZ8B4HPRDUdigbrXRURERCTsdKVTRERERMJOSaeIiIiIhN2gaAdwKPLy8rywsLDP5ZuamsjMzAxfQANIItUVEqu+qmvvXn/99Wp3HxHmkCTkQNrfRPqdhcSqr+oav8LR/sZ00llYWMjy5cv7XL64uJiFCxeGL6ABJJHqColVX9W1d2a27YNLSX85kPY3kX5nIbHqq7rGr3C0v+peFxGRPjOzc83snrq6umiHIiIxRkmniIj0mbs/5u7X5uRoVUMROTBKOkVEREQk7JR0iohIn6l7XUQOVkwPJBKR2BYIOjtrW9hZ28KmqiZKa5rZUdPCjtoWdtS0cO6EIAujHaS8i7s/BjxWVFR0TbRjEZH+94viTTz4yla+d2xyvx9bSaeIhFVbZ4C3ttdSWtNCRX0rlfWtVDa0UVHfSkllI/Wt7yzXPCjJGJ2bztjcDI6fksew5OooRi4iknhqW9rZ3dQOZPT7sZV0isghq2lqZ9ueZkr3NLOxspGNFQ3sqG2huqGN6sZ22gPBfWWz0geRn53OyKw0PjR7NHPG5TIqJ52pI4cwOieD5CTbV7a4uDgKtZH3Y2bnAudOmTIl2qGISBh0dDqpg8Jz96WSThHpM3enor6NjZUNrC9v4Kk1u9i2u4nqxvZ9ZZIMJgzPZPywwUwdmUXekFTmTRjKtPws8rPTyUjt/y4biRx1r4vEt/ZAgNRkJZ0iEgHBoFNa08yuulbK61opq2lmc1UTm6ub2FTZSEPbO93hh43JZtHMfMYNG8z0/CxG56YzecQQ0lOUWMYrXekUiW8dnU6Kkk4R6S/uTk1zR2gATyOV9W2s39VAaU0z63bWvyuxBBiVnc7EvEw+etRYpo4cwpSRWRQMzaBgaAZmtp93ERGRWNMeCKp7XUQOXG1zO8Ubqijd08zOuhbKalpCo8VbaekIvKvs0MEpTBoxhI/MHcthY7IpGDqYUTldg3rUJR7fzGwm8EUgD3jO3X+xv7LqXheJb+2BICnJ4bmYoKRTJE40t3fy1OpdlFQ2sqO2hY0Vjawtr9+3P29IKmNzM5iWn8XC6SMZm5vBmNx0JgzPpGBoBkPSBumqZYSY2f3Ah4FKdz88HMcxs7OAO4Bk4F53//7+juPu64DPmlkS8H8f8J7qXheJY+2dwVD3evADyx4oJZ0iMai5vZN15fWsKqtj1Y56Vu+oY2NlA0HvmnZoVE46E4YP5ounTeWUGSOZMSpL91kOLA8APwN+E47jmFkycBdwOlAGLDOzxXQloLf2OMZn3L3SzM4DvhY6nogkqI5AkLRBSjpFEkp7Z5Bdda3sCE2eXtXYRnVDG0s3tLLmmWcIBB2AvCFpzB6bzZmH5TOvcBgnTMl717RDMvC4+4tmVhjG48wHStx9M4CZPQKc7+630nVltLdjLQYWm9kTwO/e5z3VvS4Sx9650tn/lHSKDBAt7QFW7ajjnxurWLplD8u27iGUV+6TnpJETopz1QkTObpwGLPH5pCfnaZucelpLFDa7XkZsGB/hc1sIXABkAYseZ9y1wLXAuTn5/d5HtXGxsaEmnM1keqrusaf6j0tJBs0Ngb6vb5KOkWiwN3ZUdvCW6W1LN2yh5dKqtm2u5lA0EkymDEqm08eV8jM0dmhey8zGJGVRmZqMi+88AILF86MdhUkjMzs78CoXnZ9w93/1pdD9LLNe9nWtcO9GCj+oIO6+z3APQBFRUW+cOHCPoTSNcl/X8vGg0Sqr+oaf25f/RK5g1MZMqS53+urpFMkAjoCQUoqG3lpYzXryutZvq2G7XuaAchISebYycP50OFdq/PMmzCUYZmpUY5YosndFx3iIcqAcd2eFwA7D/GYIhLnOgNBKhvaGJ3T/0tggpJOkbAqqWzgkaWlPLGqnPK6VqBrzssZo7O4+sSJzB03lOmjssI2J5okrGXAVDObCOwALgUuj25IIjLQPb2mgvK6Vm45byxUre/34yvpFOlnrR0B/ri8lIde3c6GigbM4JTpI/nyomksmDSMCcMzox2iRJmZPQwsBPLMrAz4prvf11/HcfdOM7seeJquEev3u/uafquAiMSlqoauiyPzC4exoqr/jz+gkk4zmwR8A8hx94uiHY9IX3UGgtz21Hr+sb6S0poW2juDHDYmm1vOncVpoWUiRfZy98vCfRx3X8L7DAoSEempI9B163fKQF+RaH+TFB/gBMWbgavM7E/9FZdIOHUEgqwsq+XuFzbz7NoKTpk+gkUz8zlm0nBOmjZCUxeJiEjMaA90zc0ZCysSPUCPSYoPZoLifoxHJKyeXVvBzX9bTXldK0kG3zx3Fp8+fmK0wxIJK61IJBK/OvYmnUkD/ErnfiYpPuAJikUGsmDQeXxVOXcXb2JteT2T8jL5wUVHcOqMkeQNSYt2eCJhp8nhReJXRyDIoCQjKUy9dOG+p/NAJygeDnwXmGtmN4aS055lrgVu4P/bu/Pwuq7y3uPfV7ItWeOxrMmWPA9xbCe2E2WCEJTJcSkZCElIKG0YfSkNodxLbkmhDAEKbWkp3KSlBlIILQk0QIhDijMQNUAGnDjxbMeTbEuyBmuep/PeP86xIwtZlm0dnen3eR49Pmfvtc9ey1vaerXWXuuFQCAQOK2FS5NlYVdIrrbCxLR3T/Mg397cS2OPUzDVuO2cyaye40zq2Me2V/ZF9NxDJdO1Taa2iohEW/+gRywbEUQ+6DzdBYobgY+O9oFnujgxJM/CrpBcbYXItre9p5+/fXInD//+MJNTjftuXMYdF8+O6A/maJLp2iZTW0VEoi2UAjNycxEiHXRqgWKJa5sONfOx/9jE0Y5ebisr5QNvnce5M3KiXS0REZFx1z8YjOi60ZEOOrVAscSl+vYe7v/1Xv7rlSrM4Lt3llF+TmG0qyUSdZpIJJK4+geD8TG8frJFirVAscSTjt4B7n74NSp21xN0WL20iE9eu1i9myJhmkgkkrji5pnOky1SrAWKJR64O49sPMz/e3YPde29/Hn5Am5cWcLiouxoV01ERGRC9A3G9zOdIjFv06Fm/uW5fTyzs46VswJ8/bYVvGVBfrSrJRKTNLwukrj6B+JkeF0k3gwMBvnIQ6/w3O4GcqdO5u6rF/GJqxcpi5DIKDS8LpK4egeCpMXxRCKRmOTu/M0vtvHc7gb+vHwBd125kMw0/TiIiEjy6ugdICs9cr8L9VtWks7Gyia+uH4726rbuGpJIad1UMcAACAASURBVH95zSLSJqVGu1oicUHD6yKJq6NngPysjIh9fnRWtxaJkp1H2rj12y9SebSLT1y9iO/8WZkCTpHT4O7r3X1tbm5utKsiIuOso3eArLTJEft89XRK0qhp6eazj20D4EcfuYTzSwNRrpFI/FFPp0jiau/pJzuCw+vq6ZSkMBh07vrRJrbXtPLFG5Yp4BQ5Q+rpFElMA4PBcE+ngk6Rs/Kjlw+y6VALH337Au58y9xoV0dk3JnZGjPbbWZ7zezTI+yfY2bPmtkWM6sws9Ih+/7OzLaFv95zivNcb2brWltbI9EMEYmSXbXtBB0WF0dufWoFnZLw/nvrEb6wfgdvXTidT1y9KNrVERl3ZpYKPAD8EbAUuMPMlg4r9nXgIXc/H7gP+Gr42D8GLgBWApcA95iZUnCJJJk36toBWDYzcj/+Cjol4f3gxUoGg87Xb12BmdbglIR0MbDX3fe7ex/wCHDjsDJLgWfDr58bsn8p8D/uPuDuncBmYM0E1FlEYsiG7bUAlE6bGrFzKOiUhPbYa9W8tL+Ju65cyIzcyP0giURZCXB4yPuq8LahNgPvDr9+F5BtZtPD2//IzDLMLB+4Eph1shPpmU6RxLSxshkgoiu6aPa6JCx358HfHSAvcwp3a1hdEttIXfg+7P2ngPvN7P3A80A1MODuT5nZRcALQAPwIjAw4knM1gJrAYqKiqioqBhT5To6OsZcNhEkU3vV1sTR2tXH6jmTjrcxEu1V0CkJ6+UDTWypauUr71rOlAim9RKJAVWc2DtZCtQMLeDuNcDNAGaWBbzb3VvD+74CfCW870fAnpFO4u7rgHUAZWVlXl5ePqbKVVRUMNayiSCZ2qu2Joae/kEGf/UrVixZQHl5aDm0SLRXv4klYf3i9WpSU4ybVg4fZRRJOBuBRWY2z8ymALcDjw8tYGb5Znbsnn8v8GB4e2p4mB0zOx84H3hqwmouIlHX1tMPQE4E1+gE9XRKgurqG+DRV6u4fGG+cqpLwnP3ATO7C9gApAIPuvt2M7sPeMXdHwfKga+amRMaXv+L8OGTgd+EJ9m1Ae9z9xGH10UkMTV3hoPOqZHLRgQKOiVBvXqwmf5B54OXz4t2VUQmhLs/CTw5bNvnhrx+FHh0hON6CM1gF5Ek9fSO0Mz1c2dEdrW0mBpeN7ObzOw7ZvYLM1sd7fpI/NpY2UxqilE2Z1q0qyIiIhLTKnY3sKI0l8VFkVsYHsYx6DSzB82s3sy2Dds+apaModz9MXf/CPB+YNSsGCKjOdzUxYzcdA2ti4iIjGJ7TSuvHGzm8kX5ET/XeP5G/j5wP/DQsQ1DsmRcS2h25UYze5zQM0dfHXb8B929Pvz6s+HjRM5IdXM3MwNal1NERGQ0v9xyBIA7L5sb8XONW9Dp7s+b2dxhm49nyQAws0eAG939q8A7h3+GhZ5k/xrw3+6+abzqJsllY2UTGw828WE9zyky7szseuD6hQsXRrsqIjIONle1sLwkh8Kc9IifK9JjjyNlybhklPIfB64Bcs1sobt/e3iB8OLE9wCBQCBwWguXJvrCrkMlU1vhxPb+dE8fBpSl1VFRUT/qcfEoma5tMrU1Xrj7emB9WVnZR6JdFxE5O8Ggs6WqlRtWzJyQ80U66BxLlow3d7h/C/jWaB94posTQ2Iv7DpcMrUVTmzvT6pfZVZeG9ddc2V0KxUhyXRtk6mtIiIT7UBjJ+09A6yYFZiQ80V69vops2SIjKdDjV08vaOOtyyI/APRIiIi8WzD9tBSSRfNzZuQ80U66DxllgyR8bSluoX+Qed9l86OdlVEEpKZXW9m61pbW6NdFRE5C7/be5R/2LCbtyyYzrz8zAk553gumfQw8CJwjplVmdmHwlktjmXJ2An8xN23j9c5RYbr6Q8CkJMe2awKIsnK3de7+9rc3NxoV0VEzlD/YJC/+NEmpmem8Z0/K5uw847n7PU7TrL9D7JkiERKd/8gAGmTYyrvgYiISMzYeKCJlq5+7rpy4YSuZ63fzJJQevpCQefUyalRromIiEhsenpnHVMmpfCxKxdM6HkVdEpCOdbTma6gUyQi9EynSHyrbe3hRy8f4vKF+WRMmdisfQo6JaF09w8yOdWYnKpvbZFI0DOdIvFrT107/+s/XqV/MMgnrl404efXb2ZJKD39g+rlFIkg9XSKxKedR9r48EOvsK26lW/dsWrC1uYcSkGnJBQFnSKRpZ5OkfjzzI46bnzgd9S19bDuTy/knedPTAai4SZ2MF8kwrr7BjWJSEREBOgdGOSHLx7ky7/cSWF2Gr+8+20UZKdFrT4KOiWhdPcr6BSJJDO7Hrh+4cKF0a6KiJzC3zy2jZ+8UsWS4mz+/pbzoxpwgoJOSTA9/UHSpyjoFBGR5La9ppWfvFLFnZfN4Ys3Lo92dQA90ykJprt/kPRJ+raW5GNma8xst5ntNbNPj7B/jpk9a2ZbzKzCzEqH7LvTzPaEv+4c7Tx6plMktrV09fEfLx3kr3+2lcmpxt1RmKV+MurplITS0z9IXuaUaFdDZEKZWSrwAHAtUAVsNLPH3X3HkGJfBx5y9x+Y2VXAV4E/NbM84PNAGeDAq+Fjm09yLg2vi8Sg+rYefvjSQX7wQiVtPQMU5aTxjfesZHpWdIfUh1LQKQmlu2+QqQENr0vSuRjY6+77AczsEeBGYGjQuRT4ZPj1c8Bj4dfXAU+7e1P42KeBNcDDE1BvERkHjR29XPNP/0NbzwBXLC7gU6sXc15JLmYW7aqdQOOQklA0kUiSVAlweMj7qvC2oTYD7w6/fheQbWbTx3jscRpeF4kd7s6zO+u4+V9foK1ngH/5kwt46IMXc35pIOYCTlBPpySY7r5BTSSSZDTSbxcf9v5TwP1m9n7geaAaGBjjsaGTmK0F1gIUFRVRUVExpsp1dHSMuWwiSKb2qq3R09nv/OfOPl6oGSA3zfjg8ilkNO6momL3uHx+JNqroFMShrvT2t1PYOrkaFdFZKJVAbOGvC8FaoYWcPca4GYAM8sC3u3urWZWBZQPO7ZipJO4+zpgHUBZWZmXl5ePVOwPVFRUMNayiSCZ2qu2Rsc3nn6Df6nYS/+g87HyBXz8qkVMHecOl0i0V0GnJIz23gEGgq6JRJKMNgKLzGweoR7M24H3Di1gZvlAk7sHgXuBB8O7NgB/a2bTwu9Xh/eLSIypb+vhxxsP881n97BmWTEffts8yubmRbtaY6agUxJGc2cfAIEMBZ2SXNx9wMzuIhRApgIPuvt2M7sPeMXdHyfUm/lVM3NCw+t/ET62ycy+RChwBbjv2KQiEYkNe+vb+aen3+BX22oJOswvyOQfb1tBZlp8hXHxVVuRUTSFg868TA2vS/Jx9yeBJ4dt+9yQ148Cj57k2Ad5s+dTRGJA/2CQ3bXt/PvvKnl8czXpk1J516pSPvDWuSwpzmZSavzNBVfQKQmjpasfgGnq6RQRkTjU3NnH61UtPP9GA4+9Vk1zVz9TJ6dy86pS7llzDvkxtObmmVDQKQnjzZ5OBZ0iIhIfBgaD7DzSzpd/uYOXD4SebJmcaqxeWszli/J5++ICZgamRrmW40NBpySMxs5eQM90iohIbKtv72HTwWae29XAywcaqWzsIjXF+PDl87j63CJWzMolY0rihWgx0yIzWwN8k9BD8N91969FuUoSZyp2NzAtYzI56THzbS0iIkmupauPLVWtbKlqYXP437q2Y50kk1lYkMXaKxbw9nMKKEmQHs2TiYnfzmPMGywyqsPNXbxtUUFMZmEQSRTKvS5yajUt3Xz/hUo2bK/lYGPX8e3z8zO5bP50zisNsLgoi8vmT4/LCUFnKiaCTsaWN1hkVC2d/XqeUyTC3H09sL6srOwj0a6LSKxo6erjiS1H2HmkjR1H2njtUAsAVy8p5D0XzWJFaYDlJbnkJnnyklgJOkfK/XvJSAXDadjuAQKBQOC0UjTFWgqrSEqmtgK0tnXQ3mu01ldTUdEQ7epEVDJd22Rqq4jEj2DQ2dvQwcsHmnhqey2/2XMUgOz0SSwpzuZj5Qu4aVUJi4uyo1zT2BIrQeeYc/+eaRo2iK0UVpGWTG0FeGzDr4FuVi1bTPllc6NdnYhKpmubTG0Vkdg1GHRaeoI8/PtD/HpXPRsrm44v0zcrbyrvvWQ2N60s4aK50/SI1yhiJeg8Zd5gkdE0dof+RpmZm9gPYYuISGS4O1XN3bxysInt1W1Ut3RT3dLNkdYeGjt6CTrAVmbnZbB6aREXzc3jknnTmZU3VYHmGMVK0HnKvMEio2kIB52z8jKiXBMREYkHzZ19bK1uZVtNK7tr23n9cMvxST9TJqVQOm0qJYGpnFucQ2FOGi21h7jp7RdywWz1Zp6pmAg6T5Y3OMrVkjhytCsIQOk09XSKRJJmr0u8cXcqG7vYUtXC7tp2dte2s72mjdq2nuNlinLSWDojhw9dPo+lM3JYNXsaqSknBpYVFUe4cE7eRFc/ocRE0Akj5w0WGaudTYNMz5xCZlrMfEuLJCTNXpdY19rVz+tVLbx+qIXXDjez+XALzeHnLyelGPMLMrl0fh5LZ+Zw7owcVs4KkJ2e3LPKJ4p+Q0vcGxgMsqMxyPIS9XKKiCSbI63dPL2jjpf2N7K9pu34ELkZLCrMYvXSYlbODrByVoAFBVlMmZQ862LGGgWdEveau/px4JYLS6NdFRERiaDegcHjz1/+/kAT+xo62XmkDQg9XnV+aS63lc1i5awA55fmqgczxijolLh3LOd6flZalGsiIiLjqaWrj9cPt/Dk1iO8dig00advMPQM/4zcdBYWZvFXa5Zw7dIiFhZmRbm2cioKOiXuNXb0AZCfpWxEIpGmiUQy3rr6Bqhp6aG6pZv9DR3sa+jgcFM3h5q6OHC0E4DUFOPyhflcdW4hK0oDnFeSS+k0LVUUbxR0Stw72hHq6Zyunk5JYma2BvgmoRVAvuvuXxu2//3APxBalg7gfnf/bnjfncBnw9u/7O4/ONl5NJFIzkRX3wCHmrrYVt1GbWs31S09vHaomZqWbtp6Bk4om50+ibnTM1lclMWtZaWsmjWN5SU5GipPAAo6Je6pp1OSnZmlAg8A1xJKtrHRzB539x3Div7Y3e8admwe8HmgjFAmuFfDxzZPQNUlwQSDTkNHLy8faOKN2nbeqAt9HWzqwofkGcxJn8SymblcPC+P4tx0ZuZOZUZuOvMLssjPmqIezASloFPi3tGOXlINcvRXsCSvi4G97r4fwMweAW4EhgedI7kOeNrdm8LHPg2sAR6OUF0lzgWDzsGmLiqPdlJxqJ9XNuymprWb7dVt7K5rP14uNcWYOz2DpTNzuGlVCaXTMlg5K5fSaRmkT06NYgskWhR0Stw70tpDIM1ISdFfxpK0SoDDQ95XAZeMUO7dZnYF8AbwSXc/fJJjSyJVUYkfvQOD7DrSzuHmLhraeznY2MWOI23sqGmjo/fNIfHUXfsozklndl4GH79qIdMypnDR3DwWF2eRNknBpbxJQafENXdnc1ULMzK17poktZH+4vJh79cDD7t7r5l9FPgBcNUYjw2dxGwtsBagqKiIioqKMVWuo6NjzGUTQTy1t6vfaegO0tzjHO12mnqc1l7naHeQ/a1B+oNvlk1LhdKsFC4pSmF2zhRmZqaQHuymJC+DFDOgBzgCA9C49yAv7o1WqyIjnq7reIhEexV0Slx76MWD7G/o5D3n6HlOSWpVwKwh70uBmqEF3L1xyNvvAH835NjyYcdWjHQSd18HrAMoKyvz8vLykYr9gYqKCsZaNhHEUnvdnaMdfeypa2f/0U72NXSwr6GTutYe6tp7aAln6jlmcqqRlzmF2XnZ3Lwgi7efU8CCgiwKstOYljH5D561jKW2RloytRUi014FnRLXnttdT1FOGmvmaghHktpGYJGZzSM0O/124L1DC5jZDHc/En57A7Az/HoD8LdmNi38fjVwb+SrLONpYDDIgaOdvLCvkdcPt3C0o5fa1h6qmrvp7h88Xi5jSirzCzKZMz2DsrnTmJ2Xway8DGYGQhN5CrPTNIlHIkZBp8S16uZuVs2ahln7qQuLJCh3HzCzuwgFkKnAg+6+3czuA15x98eBu83sBmAAaALeHz62ycy+RChwBbjv2KQiiU19A0EONnayp76DI609bNhWy+uHW44vmj49cwqleRnMzc/kisUFzM7LYH5BJgsKsijOSdfz7xI1CjolrjV29nHxPA2ti7j7k8CTw7Z9bsjrezlJD6a7Pwg8OJbzaHH4iePu7Gvo5IV9R9nf0MmBo51UNnZS1dzNYPDNx25LAlP5wOVzWVSYzQWzA8ydnqnAUmKSgk6JWwODQZq7+pT+UmQCaXH4yAgGneauPrbXtPH7A01srW5lW3UrjZ2hdYgzpqQyLz+T5SW5XH/+TBYUZrKoMJuZgakEpk5WkClxQUGnxK3ath7coSgnHbqjXRsRkbFr7uzj569V89rhFnbXtrG/oZOBcO9laoqxqDCLK5cUsmJWgCvPKaAkoJSPEv8UdErc2nUk9Bzn3PwM+g6forCISJR09A6wp66dLVWtvHygkV1HQjPJAWbmpnNOcTZXn1tEUXYaxblTuWJxPhlT9OtZEo++qyVu/Wp7LakpxrIZubymoFNEoqyxo5eGjl62NAxQ+bsD7Knv4KX9jRxq6qJ/MNSLWRKYyvKSHN59YSmXzs/jgtnT1IMpSUNBp8Stps4+lhRnk5uh9JciE0UTiULPk+8/2smOmjZ2HGlj55E2dte2U9/e+2ahV3eQOSWVi+flcfW5RZTNmca5M3IonaZhckleCjolbnX1DZCpISiRCZVsE4ncncbOPvY3dLKxsomdR9p4dmf98bUvp0xKYXFRFpcvymdRYTaz8zKo2beDG695KwVZWvNSZKiY+o1tZjcBfwwUAg+4+1NRrpLEsK6+QaZlaLkkETl77k5TZx/rN9fwmz1Hae8ZoLGzlyOtPXT1vbm4enFOOlctKeTapUUsnZnD/PxMJqWemIa3omk3hdnpE90EkZg3LkGnmT0IvBOod/flQ7avAb5JaLHi77r710b7HHd/DHgsnBnj64CCTjmprr5BSgLKRCQiZ8bdqXijgfWba3hhbyO1bT0AzM7LYGYgncVF2ZSfU8jMwFQWFGSysDCL0mkZUa61SPwar57O7wP3Aw8d22BmqcADwLWEcvtuNLPH3X2HmZ0HfHXYZ3zQ3evDrz8bPlbkpLr7BjXDU0ROy9GOXn688TCbDjaztbqV+vZepkxK4cpzCvjQnHmUzZ3GylkBDYuLRMC4/MZ29+fNbO6wzRcDe919P4CZPQLcCOxw962EekZPYKGf8q8B/+3um8ajbpK4OvsGyJiink4RGV1jRy+PbDzMk1uPsLu2nYGgs6Agk8sX5rNydoCbLyglK01/wIpEWiR/ykqAoQvZVAGXnOKYjwPXALlmttDdvz28gJmtBe4BAoFAgIqKijFXqKOj47TKx7NEb2t/0Gnt6qeloYaKiqMJ396h1FaJpniavV7f3sPPNlXzvd8eoKG9l/NLc1l7xXxuvqCEhYXZ0a6eSNIZU9BpZs8AxSPs+oy7/+Jkh42wzUfY9uZO928B3zpFmXXAOoCysjIvLy8frfgJKioqOJ3y8SzR27qnrh1/6nmuKltK+arShG/vUGqrRFMsz14fDDp76zvYXNXCU9treWZn6Imtc2fk8IXrl/GO84o1bC4SRWMKOt39mjP47Cpg1pD3pUDNGXyOyB/YWNkMwNIZuVGuiYjEgl/vquPen22lri20VmZRTho3rJjJh982j/NLA1GunYhAZIfXNwKLzGweUA3cDrw3gueTJLK9ppVAxmQWF2VFuyoiEiXBoLN+Sw2Pv17Ds7vqKQlM5R9vXcG5M3JYUpxNSop6NUViyXgtmfQwUA7km1kV8Hl3/56Z3QVsILRk0oPuvn08zidS19bDjFxl9hBJRv2DQX4eflZzd107+VlT+F9vn89dVy4kO10ZykRi1XjNXr/jJNufBJ4cj3OIHNM3EEpBNztP6+WJJJtfbjnCp/5rM939g8zOy+Dz1y/lzsvmqldTJA6knLqISGz5yx+/xv6GTtYsG2lum0hyMrM1ZrbbzPaa2adH2H+FmW0yswEzu2XYvjvNbE/4686Jq/XY7Wvo4EtP7ODjD29iZiCdb7/vQv7nnnI+8NZ5CjhF4oQWJpO48ru9R3lyay3vvWQ2t188O9rVEYkJoyXjGFLsEPB+4FPDjs0DPg+UEVph5NXwsc0TUfex2FrVyq3/9gI9/UHetaqEL9+0nEytqykSd/RTK3Hl7zfsJid9Ep9afU60qyISS06ajONYAXevDO8LDjv2OuBpd28K738aWAM8HPlqj87dWb/lCJ945DVSzHjy7rexdGZOtKslImdIQafEjZ+/VsXmwy389TuWkJc5JdrVEYklZ5KMY7RjS8apXmestrWHrz+1m0dfrWJxURZ/+67zFHCKxDkFnRIXGtp7+eL6HcyZnsFtZbNOfYBIcjntZBxncmw4I9xagKKiojFnizrdzFL7Wgb59uZeGnuc6+ZM4tZzgnRUbqGicswfEVXJlElLbU1ckWivgk6Jeb94vZovPbGDzt4BHv7IpQQy1MspMszZJOOoIrTk3dBjK0Yq6O7rzOwIcH12dvaFY80WdTqZpb77m/18+aWdFOek8x/vW8FbFuSP6bhYkkyZtNTWxBWJ9mr2usS0nv5B/uqnWyjOTeenf/4Wzp2h4TWRERxPxmFmUwgl43h8jMduAFab2TQzmwasDm+Lig3ba5mXn8lT//uKuAw4ReTkFHRKTPv7X+2mpz/IPdctUSo7kZNw9wHgWDKOncBP3H27md1nZjcAmNlF4eQdtwL/Zmbbw8c2AV8iFLhuBO47NqnoJOda7+5rc3Mjk4K2pqWHlbMC5GiRd5GEo+F1iVnBoPPz16oIZEzm8oXq8RAZzUjJONz9c0NebyQ0dD7SsQ8CD47lPGZ2PXD9woULz7yyJ1Hb2kNdWw8zA+nj/tkiEn3q6ZSYdLCxk9X//DzNXf189o+XkqrFn0ViQiR7Or/x9BtMSjVNFhRJUOrplJiyt76dr/xyJ8/tbiA1xfin21bwrlVRX71FRMIi0dPZ0TvAN595g0c3VXHLBaXMmZ45bp8tIrFDQafEhL6BII9sPMSXnthB/6Dz/rfM5V2rSlgxS89xisQSd18PrC8rK/vI2X5WMOg88Nxevv0/++jsG+Sacwv57DvPHYdaikgsUtApUbetupW7frSJysYulhRn8607VrG4KDva1RKREYxXT2d9ew/3/nQrz+6q54rFBXz07fM1W10kwSnolKgIBp3tNW2s+81+nthSw+TUFO67cRl/cskcPb8pEsPGo6ezd2CQu370Gq9UNnH31Yv45DWLMNPPvUiiU9ApE25fQwd/9egWXjnYzKQU487L5vLn5QsoytGMVZFk8MBz+/j9gSb+4ZbzuVWThkSShoJOmTCHm7r44vodPLOzjqy0SXzpxmVct6yYQgWbInHjbIfXDzZ28q1n93BeSa4CTpEkE1NBp5nNBz4D5Lr7LdGuj4yPvfXt/Py1ar732wP0DQR5/1vmsvaK+cwMTI121UTkNJ3t8Pp963eQnT6JL9ywbJxrJiKxblyCTjN7EHgnUO/uy4dsXwN8E0gFvuvuXxvtc9x9P/AhM3t0POol0fNGXTsVu+vZsL2OVw82YwZXLyniCzcspXRaRrSrJyJR0D8Y5Ld7j3LHxbO5cM60aFdHRCbYePV0fh+4H3jo2AYzSwUeAK4FqoCNZva4u+8ws/OArw77jA+6e/041UeipKtvgH/YsJt//10lAPMLMrn3j5Zw8wWlFGSnRbdyInLWzmZ4/VBTF70DQc4riUwKTRGJbeMSdLr782Y2d9jmi4G94d5LzOwR4EZgh7tvJdQzKgmitbufH288xPd+e4C6tl6uWFzAX79jCUuKc6JdNREZR2czvL6vvgMI/TEqIsknks90lgCHh7yvAi4Z7QAzmw58BVhlZve6+/DeUMxsLXAPEAgEAlRUVIy5Qh0dHadVPp5NVFu7B5wdjYM8trefw+1BZmYZnypLZ3l+F7W7NlG7K+JVAHRtE1UytTUZ7D/aCcD8gqwo10REomFMQaeZPQMUj7DrM+7+i5MdNsI2H+087t4IfPQUZdYB6wDKysq8vLx8tOInqKio4HTKx7NItrWquYuX9zfx3O56frWtloGgMznV+Pb7LmTN8pG+TSJP1zYxJVNbk8EzO+ooCUwld+rkaFdFRKJgTEGnu19zBp9dBQxdD6MUqDmDz5Eoc3de3NfIS/sbeflAEy8faAJgWsZkbrtoFtefP5OlM3P0i0RETqq7b5BNh5r5WPn45WwXkfgSyeH1jcAiM5sHVAO3A++N4PlknNW19fDrXfX858sH2VbdRorB4qJs/ve1i1m9rIjFhdmkKHuQSFI504lE+xo6CDosnannvEWS1XgtmfQwUA7km1kV8Hl3/56Z3QVsILRk0oPuvn08zifjz91p7uqnsrGTrVWt/Nerh9lW3QbAvPxMPn/9Um65sJTsdPVmiiSzM51IdKS1B4DSaVqfVyRZjdfs9TtOsv1J4MnxOIdExubDLfzwpYO8erCZA+GH/CE0u/Sv1izhqiWFLC7KUl5kETkrta3dABTnKgOZSLKKqYxEEnmNHb08v6eB3x9oYmNlM3vrO0ifnMLF86Zzx8WzWFiYxZzpmcydnkmqhs5FZJzUtPYwKcXIz9R6vSLJSkFngnJ3Gjt6qWzsYmNlE1urW9l4oIn69l4AcqdO5vzSXG6/aBY3rizRwu0ice5UGeDMLI1QAo8LgUbgPe5eGV6q7lHgIuD77n5XJOpX29pDUU66ngMXSWIKOuOYu9PY2cfe+g721Hewt66dvQ0dHG7qpq61i94NzxwvWxKYyoVzpnF+aYC3LpzO8pm5uvmLeuQN/AAACdpJREFUJIjRMsANKfYhoNndF5rZ7cDfAe8BeoC/AZaHvyLiSGs3MzS0LpLUFHTGgWOTfI529FLV3MWu2nZe3t/ElqoWmrv6j5fLSpvErLwMVs0O0N3cx6XnLWbO9AyWzczVc1Qiie2kGeCGlLkR+EL49aPA/WZm7t4J/NbMIrqWUW1rD+eVBiJ5ChGJcQo6Y0RH7wAHGjrZf7SDfQ2dbD7cQmVjJ529g7R199M3GDyhfHFOOquXFnNOcTYLC7NYVJRFcU768Qk/FRUVlF8+LxpNEZGJN5YMcMfLuPuAmbUC04Gjka6cu1PT0sPqZfrjVySZKeicYMGgU93SzRt17eyqbafyaCeVjZ1sPtx6PLBMMVhQkMWK0gBZ6ZPITp9EUXY6BdlpzAyks6gomxwtXSQibxpLBrjTzhL3BycJpSFeC1BUVDTmFKUvHeqkb9DoqK+ioqLudE4Zl5Ipfavamrgi0V4FnRHW3tPP1qpW3qhr51fba9le00Z7z8Dx/QXZacybnsltF5Vy+cJ85hdkMWd6BmmTUqNYaxGJM2PJAHesTJWZTQJygabTOYm7rzOzI8D12dnZF441RenPvvMU0M89t76daZlTTueUcSmZ0reqrYkrEu1V0DkOBgaDVLd0U9nYxYv7Gtle00p9Wy917T20DHnmcnrmFN6xfAYrZwdYWJjF0hk5ZKbpEojIWRtLBrjHgTuBF4FbgF+7+2n1dJ6pytYgc6ZnJEXAKSInp4jnNLg7e+o7qG7uZtOh5hOGx/sHQ/fuFINlM3OZPT2Di+flUZidxvKSXM4pzqZYy4WISASEn9H8gwxwZnYf8Iq7Pw58D/ihme0l1MN5+7HjzawSyAGmmNlNwOphM9+Hnuu0MhK5O9sbB/nTywrPooUikggUdJ7E/oYOdh5pZ39DB/saQpN79jd00Nk3CLz53OWc6RlcfW4R8wsyKQlM5bzSXD1vKSITbqQMcO7+uSGve4BbT3Ls3LGe53Rzr7f1DDDoSn8pIkkedA4MBjnU1EVtaw9tPQO0dPVxuLmLLVWt/GbPmxM6SwJTmV+Qya1ls1g6M4d5+ZmcOyOHLA2Ni4iMqjX8iFEgQ0PrIskuaaKmTYeaeb6qnxf/eyf7GzrZ19DBocYuBoInPtKUmmKUBKby7gtK+cBb5zK/IJOMKUnz3yQiMqrTHV5v6e4DIDBVI0AiyS5poqlP/WQz+4/2MSW1kjnTM1hUmMV1y4qZn59JybSp5E6dTE76ZIpz05mcmhLt6oqIJITm4z2dCjpFkl3SBJ3/fPtKdm3ZxM3XlTNJQaWIyBk53Wc6V80O8NlL01kyIyeyFRORmJc00df5pQEKM1IUcIqInAV3X+/ua3Nzc8dUPid9MgsDqXoGXkSSJ+gUEZGzZ2bXm9m61tbWaFdFROJMTAWdZpZpZj8ws++Y2Z9Euz4iInKi0+3pFBE5JqJBp5k9aGb1ZrZt2PY1ZrbbzPaa2aeH7LoZeNTdPwLcEMm6iYiIiMjEiXRP5/eBNUM3mFkq8ADwR8BS4A4zWxreXQocDr8ejHDdRETkNGl4XUTOVESDTnd/nlC6taEuBva6+3537wMeAW4M76siFHhGvG4iInL6NLwuImcqGtMJS3izNxNCgeYl4dc/A+43sz8G1o90sJmtBe4BAkCPmW0/jXPnA0dPWSoxJFNbIbnaq7aObE4kKyInevXVV4+a2cExFk+m71lIrvaqrYlr3O+/ZxV0mtkzQPEIuz7j7r842WEjbHMAd+8EPjDaOd19HbDudOp5/MRmr7h72ZkcG2+Sqa2QXO1VWyUWuHvBWMsm23VMpvaqrYkrEu09q6DT3a85g8OqgFlD3pcCNWdTDxERERGJbdF4bnIjsMjM5pnZFOB24PEo1ENEREREJkikl0x6GHgROMfMqszsQ+4+ANwFbAB2Aj9x99N5LvNsnNGwfJxKprZCcrVXbZV4k2zXMZnaq7YmrnFvr7n7eH+miIiIiMgJtCyRiIiIiERcUgSdo2RAiltmNsvMnjOznWa23cw+Ed6eZ2ZPm9me8L/TwtvNzL4V/j/YYmYXRLcFp8/MUs3sNTN7Ivx+npm9HG7rj8PPCGNmaeH3e8P750az3qfLzAJm9qiZ7Qpf38sS/Lp+Mvw9vM3MHjaz9ES9tsko0e6/uvcm7r0Xkuv+G417b8IHnTZ6BqR4NgD8H3c/F7gU+Itwuz4NPOvui4Bnw+8h1P5F4a+1wL9OfJXP2icIPQd8zN8B3wi3tRn4UHj7h4Bmd18IfCNcLp58E/iVuy8BVhBqc0JeVzMrAe4Gytx9OZBKaHJhol7bpJKg91/dexP75zMp7r9Ru/e6e0J/AZcBG4a8vxe4N9r1ikA7fwFcC+wGZoS3zQB2h1//G3DHkPLHy8XDF6GltZ4FrgKeILTe61Fg0vDrTGiS2mXh15PC5SzabRhjO3OAA8Prm8DX9ViyiLzwtXoCuC4Rr20yfiXD/Vf33sT5+Uym+2+07r0J39PJyBmQSqJUl4gId3OvAl4Gitz9CED438JwsXj/f/hn4P8CwfD76UCLh1ZDgBPbc7yt4f2t4fLxYD7QAPx7eDjru2aWSYJeV3evBr4OHAKOELpWr5KY1zYZxfX356no3gsk1s9n0tx/o3XvTYag86QZkBKBmWUBPwX+0t3bRis6wra4+H8ws3cC9e7+6tDNIxT1MeyLdZOAC4B/dfdVQCdvDuWMJJ7bSvjZqBuBecBMIJPQkNVwiXBtk1HCXi/de0+QKD+fSXP/jda9NxmCzoTNgGRmkwnd9P7T3X8W3lxnZjPC+2cA9eHt8fz/8FbgBjOrBB4hNMzzz0DAzI5l1RranuNtDe/PBZomssJnoQqocveXw+8fJXQTTMTrCnANcMDdG9y9H/gZ8BYS89omo3j//hyR7r0J+/OZTPffqNx7kyHoTMgMSGZmwPeAne7+T0N2PQ7cGX59J6HnjY5t/7PwbLtLgdZjwwWxzt3vdfdSd59L6Pr92t3/BHgOuCVcbHhbj/0f3BIuHxd/fbp7LXDYzM4Jb7oa2EECXtewQ8ClZpYR/p4+1t6Eu7ZJKuHuv7r3Jua9F5Lu/hude2+0H2adiC/gHcAbwD7gM9Guzzi16XJCXdtbgNfDX+8g9IzFs8Ce8L954fJGaBbpPmAroRlrUW/HGbS7HHgi/Ho+8HtgL/BfQFp4e3r4/d7w/vnRrvdptnEl8Er42j4GTEvk6wp8EdgFbAN+CKQl6rVNxq9Eu//q3pu4995wG5Lm/huNe68yEomIiIhIxCXD8LqIiIiIRJmCThERERGJOAWdIiIiIhJxCjpFREREJOIUdIqIiIhIxCnoFBEREZGIU9ApIiIiIhGnoFNEREREIu7/A24cvZRAfBDRAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x432 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.ticker import NullFormatter  # useful for `logit` scale\n",
    "\n",
    "# Fixing random state for reproducibility\n",
    "np.random.seed(19680801)\n",
    "\n",
    "# make up some data in the interval ]0, 1[\n",
    "y = np.random.normal(loc=0.5, scale=0.4, size=1000)\n",
    "y = y[(y > 0) & (y < 1)]\n",
    "y.sort()\n",
    "x = np.arange(len(y))\n",
    "\n",
    "# plot with various axes scales\n",
    "plt.figure(figsize=(10,6))\n",
    "\n",
    "# linear\n",
    "plt.subplot(221)\n",
    "plt.plot(x, y)\n",
    "plt.yscale('linear')\n",
    "plt.title('linear')\n",
    "plt.grid(True)\n",
    "\n",
    "\n",
    "# log\n",
    "plt.subplot(222)\n",
    "plt.plot(x, y)\n",
    "plt.yscale('log')\n",
    "plt.title('log')\n",
    "plt.grid(True)\n",
    "\n",
    "\n",
    "# symmetric log\n",
    "plt.subplot(223)\n",
    "plt.plot(x, y - y.mean())\n",
    "plt.yscale('symlog', linthreshy=0.01)\n",
    "plt.title('symlog')\n",
    "plt.grid(True)\n",
    "\n",
    "# logit\n",
    "plt.subplot(224)\n",
    "plt.plot(x, y)\n",
    "plt.yscale('logit')\n",
    "plt.title('logit')\n",
    "plt.grid(True)\n",
    "# Format the minor tick labels of the y-axis into empty strings with\n",
    "# `NullFormatter`, to avoid cumbering the axis with too many labels.\n",
    "plt.gca().yaxis.set_minor_formatter(NullFormatter())\n",
    "# Adjust the subplot layout, because the logit one may take more space\n",
    "# than usual, due to y-tick labels like \"1 - 10^{-3}\"\n",
    "plt.subplots_adjust(top=0.92, bottom=0.08, left=0.10, right=0.95, hspace=0.25,\n",
    "                    wspace=0.35)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is also possible to add your own scale, see `adding-new-scales` for\n",
    "details.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
